# Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Information

arXiv: https://arxiv.org/abs/2011.13563 · HTML: https://mlsanity.com/paper/2011.13563/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [Predicting Levels of Household Electricity Consumption in Low-Access Settings](https://mlsanity.com/paper/2112.08497/) (2021)
- [A step towards the integration of machine learning and classic model-based survey methods](https://mlsanity.com/paper/2402.07521/) (2024)
- [Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal](https://mlsanity.com/paper/2211.06277/) (2022)
- [Poverty rate prediction using multi-modal survey and earth observation data](https://mlsanity.com/paper/2307.11921/) (2023)
- [Use of Boosting Algorithms in Household-Level Poverty Measurement: A Machine Learning Approach to Predict and Classify Household Wealth Quintiles in the Philippines](https://mlsanity.com/paper/2407.13061/) (2024)
- [Towards Public Administration Research Based on Interpretable Machine Learning](https://mlsanity.com/paper/2601.06205/) (2026)
- [Joint Utilization of Geospatial and census proxies for Autoencoder-Assisted Downscaling (JUGAAD) of socioeconomic indicators in India](https://mlsanity.com/paper/2607.20559/) (2026)
- [Crowd-Sourced Geographies of Income: Using Google Maps Points of Interest as High-Frequency Proxies for Sub-Municipal Income Estimation in Sao Paulo, Brazil](https://mlsanity.com/paper/2608.07871/) (2026)

## Abstract (arXiv)

Access to accurate, granular, and up-to-date poverty data is essential for humanitarian organizations to identify vulnerable areas for poverty alleviation efforts. Recent works have shown success in combining computer vision and satellite imagery for poverty estimation; however, the cost of acquiring high-resolution images coupled with black box models can be a barrier to adoption for many development organizations. In this study, we present a interpretable and cost-efficient approach to poverty estimation using machine learning and readily accessible data sources including social media data, low-resolution satellite images, and volunteered geographic information. Using our method, we achieve an R2 of 0.66 for wealth estimation in the Philippines, compared to 0.63 using satellite imagery. Finally, we use feature importance analysis to identify the highest contributing features both globally and locally to help decision makers gain deeper insights into poverty.